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Artificial Intelligence-Based Mobile Application for Sensing Children Emotion Through Drawings.

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This study introduces an AI-powered app that analyzes children's drawings to detect emotions. The Emotion Sensing Recognition App (ESRA) shows potential for understanding children's feelings, aiding in early mental health support.

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Area of Science:

  • Child Psychology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Children experience a range of emotions, but expressing them can be challenging.
  • Identifying unexpressed emotions is crucial for addressing children's needs and preventing mental health issues.

Purpose of the Study:

  • To develop an AI-based Emotion Sensing Recognition App (ESRA) for analyzing children's drawings.
  • To assist parents and teachers in understanding children's emotional states through their artwork.

Main Methods:

  • Collected 623 drawings from a school in Doha and online sources (Google, Instagram).
  • Trained a deep learning model using the Fastai library in Python.
  • Conducted four experiments using combined datasets to classify drawings into positive or negative emotions.

Main Results:

  • The AI model achieved accuracy ranging from 55% to 79% across four experiments.
  • The Emotion Sensing Recognition App (ESRA) demonstrated potential in identifying children's emotions from drawings.

Conclusions:

  • The ESRA system shows promise for emotion detection in children's drawings.
  • Further training and evaluation with larger datasets are needed to enhance accuracy and specificity.